About the choice of the activation functions in the Multilayer Perceptron, and on what does this depends?
Rosenblatt was probably discussing a specific architecture, for which there are many.  However, for general purpose feed-forward back-propagation ANNs used for function aproximation and classification analysis, you can use whatever activation functions you want on the input-side, hidden layers, and output-side.  Examples are identity, logistic, tanh, exponential, Hermite, Laguerre, RBFN, ReLu, softmax, etc.  &quot;Automatically,&quot; likely refers to the iterative learning process, which tends to be similar to gradient descent, during which partial derivatives of prediction error w.r.t to coefficients decrease.